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Interfaces for configuring and wrapping model optimizers.
Distinguishing note: Focuses on gradient clipping and parameter-specific optimization settings.
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This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular
Enables defining optimization wrappers to select optimizers, set learning rates, and apply gradient clipping.